In [1]:

    

import warnings
warnings.filterwarnings('ignore')


    

In [2]:

    

%matplotlib inline
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

from distutils.version import StrictVersion


    

In [4]:

    

import sklearn
print(sklearn.__version__)

assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')


    

    




0.18.1

In [5]:

    

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)

assert StrictVersion(tf.__version__) >= StrictVersion('1.1.0')


    

    




1.3.0

In [6]:

    

import keras
print(keras.__version__)

assert StrictVersion(keras.__version__) >= StrictVersion('2.0.0')


    

    




Using TensorFlow backend.






    




2.0.8

In [7]:

    

import pandas as pd
print(pd.__version__)

assert StrictVersion(pd.__version__) >= StrictVersion('0.20.0')


    

    




0.20.1

In [8]:

    

# graph definition
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)

# launching the graph in a session
with tf.Session() as sess:
    result = sess.run([product])
    print(result)


    

    




[array([[ 12.]], dtype=float32)]

In [9]:

    

sess = tf.InteractiveSession()

x = tf.Variable([1.0, 2.0])
a = tf.constant([3.0, 3.0])

# Initialize 'x' using the run() method of its initializer op.
x.initializer.run()

# Add an op to subtract 'a' from 'x'.  Run it and print the result
sub = tf.subtract(x, a)
print(sub.eval())
# ==> [-2. -1.]

# Close the Session when we're done.
sess.close()


    

    




[-2. -1.]

In [16]:

    

!cat sample_iris.json
# Example for iris, model exported as Tensorflow
# gsutil cp -R 1 gs://irisnn
# create model and version at https://console.cloud.google.com/mlengine
# in a DOS shell on local machine in this folder
# gcloud ml-engine predict --model=irisnn --json-instances=./sample_iris.json
# SCORES
# [0.9954029321670532, 0.004596732556819916, 3.3544753819114703e-07]


    

    




{"inputs": [ 5.1,  3.5,  1.4,  0.2]}

In [1]:

    

!curl -O https://raw.githubusercontent.com/DJCordhose/speed-limit-signs/master/data/speed-limit-signs.zip
from zipfile import ZipFile
zip = ZipFile(r'speed-limit-signs.zip')
zip.extractall('.')


    

    




  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 1810k  100 1810k    0     0  5225k      0 --:--:-- --:--:-- --:--:-- 5518k

In [11]:

    

# Calling a Keras based model to classify speed limit signs
!curl -H "Content-Type: application/json" -X GET -d '{"url": "https://github.com/DJCordhose/speed-limit-signs/raw/master/data/real-world/4/100-sky-cutoff-detail.jpg", "model": "default" }' http://ec2-52-43-39-37.us-west-2.compute.amazonaws.com:8888


    

    




{
  "category": [
    4
  ], 
  "model": "default", 
  "prediction": [
    [
      5.188912632902998e-10, 
      1.7854775817560675e-12, 
      6.658332507774913e-23, 
      1.2231632354087196e-05, 
      0.9999877214431763, 
      5.86981815167291e-16
    ]
  ]
}